Overfitting in Machine learning is making our model learn too much from the training dataset. If we train too much on the training dataset, it learns the noise from the dataset also. So, when we build this model on unseen data then it fails to predict the results accurately. This can be solved by increasing the training data or by using regularization or ensemble techniques.
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